CAGE: Regime-Aware Algorithm Engineering for Coverage Optimization

Abstract

Coverage-selection policies that optimize the same monotone objective can occupy different quality-runtime frontiers as workload scale, budget, repetition horizon, and execution substrate change. CAGE is a regime-aware algorithm-engineering framework that holds coverage semantics fixed through a shared bitset execution substrate and studies how the nondominated strategy set changes under controlled operating conditions. We separate intrinsic workload descriptors from measured strategy response, represent each condition by its empirical Pareto set, and identify regime transitions from changes in Pareto membership or statistically supported crossovers. Across Defects4J, Epinions, a BWSN-inspired time-to-detection workload, repeated selection, and an embedded RV32 realization, we observe distinct transitions. At a 5% budget, one-exchange refinement adds 27 Chart lines at 11.3x baseline median runtime, whereas deeper search adds five Time lines at roughly 715x. On Math, CP-SAT confirms full-universe optimality at 20% and 40% and residual lower-budget headroom. Persistent preprocessing reuse first achieves confidence-interval-supported speedup at horizon 20 and reaches 1.442x at horizon 100. ModelSim shows a separate bitset-width crossover. The results demonstrate that strategy nondominance is workload- and condition-dependent, supporting regime-aware algorithm engineering rather than a universal policy ranking.